Probabilistic diffusion magnetic resonance imaging fiber tracking using a directed acyclic graph auto-regressive model for positive definite matrices

Authors

  • Zhou Lan Center for Outcomes Research and Evaluation Yale University, New Haven CT 06510, USA
  • Brian J Reich Department of Statistics North Carolina State University, Raleigh NC 27695, USA
  • Dipankar Bandyopadhyay Department of Biostatistics Virginia Commonwealth University, Richmond VA 23298, USA

Keywords:

Directed Acyclic Graph Auto-Regressive Model; Diffusion MRI; Fiber Tracking; Positive Definite Matrices

Abstract

Diffusion magnetic resonance imaging (MRI) is a neuroimaging technique for probing the anatomical structure of tissues through quantification of the water diffusion process. Using diffusion MRI to reconstruct white matter fiber tracts and assess tissue connectivity, also known as fiber tracking, is arguably the most important applications of diffusion MRI. Although a number of innovative and compelling techniques are available for fiber tracking, only a few provide an elegant evaluation of the statistical (spatial) uncertainties. In this paper, we propose spatial modeling of positive definite diffusion tensor matrices via a directed acyclic graph auto-regressive model and develop an efficient probabilistic fiber tracking algorithm. We illustrate our proposed method via numerical studies and application to a real dataset.

Journal of Statistical Research 2021, Vol. 55, No. 1, pp. 147-158

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Published

2021-12-09

How to Cite

Lan, Z. ., Reich, B. J. ., & Bandyopadhyay, D. . (2021). Probabilistic diffusion magnetic resonance imaging fiber tracking using a directed acyclic graph auto-regressive model for positive definite matrices. Journal of Statistical Research, 55(1), 147–158. Retrieved from https://www.banglajol.info/index.php/JStR/article/view/56584

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